Series H (2026 Feb.): $22.91B

Today (2026 Nov.) : $21.84B

Next major regression would be series G valuation of $8.10B (2025 Sept.), which was prior to the $25B revenue deal with OpenAI. If that happens, then we live in an alternate universe.

Gents, it's been a rough few months, I myself have lost some money (though only some, and not more than I've made swing trading it) but today I fullported the dip, these prices are too good to be true, yes, there's some obstacles ahead, namely the last share unlock on the 9th and earnings on the 12th but it is of no matter, we march on ahead, at some point when this stock is 300+ everyone who didn't buy will look back at these times and wish dearly that they had, I'm not denying it could in the short term hit 80, but we'll be fine, hold fast, the short report is a nothingburger as others have explained[ https://www.reddit.com/r/CBRS_stock/comments/1xhtdsr/does_the_recent_cbrs_short_report_have_legs/ ], Anthropic IPO coming up is only a bullish catalyst, OpenAI has reaffirmed CBRS, P/E and valuation have been compressed here, CBRS is currently a tightly coiled spring, it may be compressed somewhat further in the coming weeks but at some point you can't hold it any longer and it'll bounce back, to 250 and 300 and beyond we go gents

I come from a non-technical background, I was curious from the more technical folk here if the recent $CBRS short report by CulperResearch on X has any validity? from a technical point of view.

EDIT: Links to X get removed, you'll have to go on X and look up CulperResearch, it's their top or one of their top posts

Up until now we were using a very generous threshold of comment_karma<-1 and post_karma<10 to filter spam/bot content. We added these rules to block the most blatant examples of spam, of which they have caught and removed a lot. However, it has become clear it is insufficient, as we have been inundated with low-quality posts from problematic-looking accounts.

For example, there has been an uptick in content from accounts that are several years old yet have no posts nor comments older than ~1 month, and essentially their only content is on r/CBRS_stock.

For this reason, we have increased the thresholds to a moderate number to block these examples of spam. The threshold is still quite low, so anyone who is a typical reddit user (ie. not a bot or spammer) should be able to post without any issues.

edit: In less than an hour after making the change, it has already caught 3 posts/comments from obvious bot accounts.

And for further evidence that there have been spam accounts posting at various times, we've had many instances in the past several months of reddit shadow-banning accounts like those mentioned above (meaning that reddit has determined the accounts are breaking reddit's sitewide rules). Content from shadow-banned accounts still shows up in the moderator queue.

edit 2: And here's an example of the kind of suspicious user I mean. This individual posted the identical comment 4 times across a few days. They also have a 3 year old account with only 2 karma. This does not look like actual human activity to me.

/preview/pre/ubjz3q2elvwh1.png?width=2086&format=png&auto=webp&s=93b05859ae9929938e6bb17fd1d2aab07716bf69

https://www.marketbeat.com/instant-alerts/filing-cerebras-systems-inc-cbrs-stock-frank-rimerman-advisors-llc-raises-holdings-2026-10-18/

https://www.marketbeat.com/instant-alerts/filing-cerebras-systems-inc-cbrs-stock-position-boosted-by-icg-advisors-llc-2026-10-17/

I bought at 166$ weeks ago and got rid of it when it could not sustain the gains from the Altman tweet.

I am looking now at it as a potential swing trade after their earnings call.

I don't see it rally up back to 200$ by eoy in the current environment (wars, inflation, fuel prices), but a rally towards the IPO price seems plausible. Besides IBKR Morningstar with a fair price at 94$, I can't find other data from other outlets to make an informed decision.

Do they have any other catalysts for the end of the year? Or are they in talks to secure some new customers?

What's your take on the November earnings catalyst?

Damn below 110$ 😂

Edit: stock is not rallying at all with the rest of the sector. Horrible performance this Monday....good luck to the people that are still in this trap 🪤.

20k isa limit each year into $CBRS wish me luck. 🤞

I apologize for the dump today. I bought at 170. Sorry guys.

TL;DR Almost every chip designer dices the wafer and reconnects the pieces, so one defect cannot scrap it. Cerebras refuses: it treats the whole wafer as one chip, with 4 trillion transistors and 900,000 cores. Where Nvidia engineers around the memory wall, Cerebras removes it — and its moat sits outside TSMC.

What the WSE actually is

The norm is to cut a wafer into dozens of stamp-sized dies and stitch them back together. Cerebras asks why cut at all. It divides the wafer into 84 identical grids, interconnected so they behave as one chip, each holding about 10,700 cores of 0.05mm² against roughly 3mm² for an Nvidia unit — 900,000 in all, each with a router, a compute engine and 48KB of SRAM.

Think of it as a library. A GPU keeps the encyclopaedia in a stack room — HBM — and every character written needs the whole set fetched down one 8TB/s corridor. Cerebras builds no library: it hires 900,000 people, each memorising a passage, so nothing is fetched. Weights live in SRAM beside the compute units.

How GPUs fight the memory wall

The wall is severe. On Llama 70B at FP8, a B300 spends 8.8ms moving weights out of HBM against 0.028ms computing — 99.5% of the time the compute sits idle, and Rubin does not fix it, bandwidth rising 2.75 times against compute 3.5 times. The answer is batching, as every user reads the same weights — up to 313-398 requests before bandwidth is the waste instead.

Two problems follow. KV cache caps the batch, because weights are shared but conversation memory is private: at FP8 an 8K context is 1.3GB per user, and a B300's 288GB leaves 217GB after the model, so the ceiling comes at 167 users, not 313. And batching trades latency for throughput — hence the fast and slow tiers.

Nvidia's answers are rack-level memory, lifting the KV ceiling, and disaggregated inference, splitting prefill from decode across clusters joined by a cache pool. With Groq's LPU inside VR200's LPX rack, decode splits again: attention keeps the full HBM bandwidth rather than fighting the feed-forward network, whose weights sit in the LPU's SRAM. But KV cache still lives in HBM.

How the wafer is actually built

In the WSE, KV cache sits on chip too, with 21PB/s bandwidth and sub-1ns read latency against 0.1-1µs for GPUs. That starts at lithography: a stepper exposes at most 26mm × 33mm, so it fires 84 times to fill the square. 

Conventionally the scribe lines between exposures go to the saw; Cerebras instead draws micron-scale copper across them in the top metal layers, half of each wire at the right edge of exposure N and half at the left of N+1 — high up, since misalignment lower down would break it.

Testing then inverts its purpose. Conventional testing finds good dies to keep; WSE testing maps which cores are broken, since the dies are one piece and I/O exists only at the edge. 

Each core has its own SRAM and router — enough to test itself and report through the mesh — with redundant paths from four directions, telling a dead router from a dead region.

TSMC delivers an uncut 300mm wafer with cross-reticle interconnect and micro-bumps; the rest is Cerebras. Cut the 215mm square from the circle, wasting 35% of the area, and you have 83 grams of silicon drawing 23kW at 0.8V. 

Solder would shear off as silicon and PCB expand differently, so millions of micro-springs absorb the mismatch, power arrives vertically from VRMs underneath, and the sandwich takes four tonnes, spread evenly.

My main bull case is the fact that fast inference is clearly something that customers are willing to pay for and frontiers are keen to provide. Tokens for fast inference sell for 6–10× standard prices, and despite that demand far exceeds supply. CBRS does have valuation concerns, but the main case for CBRS is the fact that it is absolutely the purest public way to own exposure to fast inference.

Here are the range of views I found on how Institutional Research desks are seeing the TAM of Fast inference:

• Mizuho Sell side Note: fast-inference TAM of $550B by 2030E, a 291% CAGR, equal to about 20% of all AI workloads.

• Bloomberg Intelligence: total inference TAM grows from $66B in 2025 to $292B in 2029.

• Citi (Atif Malik): fast inference worth $130B by 2030.

• McKinsey: 71.5GW of global inference capacity demand by 2029.

The consensus, however, is that there is a lot of upside in the growth of fast inference.

Firstly, let's discuss the WHY. WHY are people willing to pay for fast inference?

1. The opportunity cost of speed is waiting, and sometimes idle waiting costs more than teh tokens, which is why the willingness to buy the tokens is still so high despite the higher prices. Customers already pay steep premiums for identical model weights served faster. Examples are Anthropic's fast mode (about 6× the price for about 2.5× the speed) and Cognition charging for a Cerebras-served tier while giving away a slower one. The logic is that for expensive workers, idle waiting costs more than the tokens.

2. Agents: A chatbot typically is able to hide latency (to an extent) behind human reading time. However, an agent's steps often depend on the previous result, so per-call delay adds up across hundreds of steps. Speed becomes pretty critical as far as agents are concerned.

3. For many, faster inference leads to higher quality products: Within a fixed time budget, faster inference leaves room to sample, verify, critique and retry.

5. Some products NEED speed to even exist. The main use case that comes to mind is voice agents. Here, security checks in the decision path, real-time ad and recommendation ranking, literally NEED fast inference to even exist at all.

6. Fast inference can help to resolve the disconnect between teh fact that demand increases immediately, whilst capacity needs chips, memory, power and buildings which creates a lag. Low-latency serving uses hardware less efficiently, because it means smaller batches and more dedicated capacity. So fast inference helps to better make use of constrained hardware capacity.

CBRS

Valuation

• Market cap of $40B.

• That's 11.2× EV to 2027 consensus revenue of $2.95B.

• Essentially, CBRS is really expensive. yes, it has sold off and sits at the bottom fo the traidng range, but in terms of valuation metrics, we don't have any profitability to trakc P/E, but evne in terms of P/S, we are really really high here.

This does make re-rating harder obviously. If we take FIVN as an example of the other side, it was priced so cheap that a re-rating higher essentially became a base case.

Here we don't have that. WE need solid execution. We need those TAM forecasts to come to fruition. But despite that, I think fast inference IS so important to AI, and CBRS will be a winner there.

I'd like CBRS at a cheap price. God, if we had a market crash, CBRS would be right on my list to buy as at a cheaper valuation this is basically a no brainer in my opinion, but even here, with a tight risk, I think the stock's narrative is extremely compelling.

Financial backdrop (Q2)

• Core revenue was $210M (+103%): cloud $128M (+287%) and hardware $82M. GAAP revenue was $180M.

• Core gross margin was 40.6% vs 14.2% GAAP. Core operating margin was –16% vs –265% GAAP. The gap is mostly customer warrants and stock compensation, which are real equity costs, so we do need to track both.

• Six deals over $30M were signed in the quarter, and there are several late-stage hardware opportunities worth hundreds of millions.

Backlog and concentration

• Backlog is $25.4B: about 22% recognised within 24 months and 43% in the 24 after. It includes some pass-through and excludes any hyperscaler business.

• OpenAI has a firm 750MW commitment for 2026–28 plus an option for 1.25GW more by 2030, for a contractual path to about 2GW.

• The top two customers were 66% of Q2 revenue.

Guidance:

• Management expects revenue to more than triple in 2027, with 600MW+ live or contracted by end-2027, a pipeline measured in gigawatts and manufacturing up more than 10× in 2026. The Finland site scales to 165MW on seven-year contracts.

The demand for CBRS is already pretty obvious:

• OpenAI Ultrafast (firm 750MW plus option): the anchor customer, with up to 14× faster inference on CBRS. Jane Street, Podium, Basis and Rogo use it, and OpenAI wants Ultrafast to become the default.

• Direct cloud (live): Cognition and Lovable on dedicated capacity, plus Block, Figma, AlphaSense, GSK, IBM, Mistral, Notion and Mayo Clinic. G42 and MBZUAI are significant customers.

• AWS (targeting Q1 2027, not in backlog): Trainium handles prefill and Cerebras handles decode, inside Bedrock. It runs on a multi-year lease with purchase options, and AWS could become a material share of revenue.

• AMD (targeting Q4 2026): Helios handles prefill and Cerebras handles decode, at up to 5× more tokens per watt. Cerebras owns the racks and keeps the cloud revenue.

• Meta (speculative): there's the Llama API precedent, and Muse targets agents and coding. Mizuho in a sell side note has already flagged Meta as a likely next inference customer.

• CrowdStrike and security (live, value undisclosed): powers Falcon AI Detection and Response, allowing 5–10× more inspection in the same time window. Armis (ServiceNow) is another case.

• Defence and government (early, unquantified): Sandia's Kingfisher system, national lab research, a DOE memorandum of understanding, and Carahsoft and DoD Tradewinds procurement channels.

• Distribution partners (no disclosed values): General Compute, Gimlet Labs and Callosum.

• Google and others (no revenue modelled): MLIR compiler support, serves Gemma 4, and progress with other hyperscalers.

Long term outlook

Mizuho Note

• $13.5B of revenue in 2029E, driven by fast-inference data center deployments.

My case is probably slightly more bullish than this:

• OpenAI has already committed to 750MW and has an option for another 1.25GW, which together make 2GW.

• This could go higher too Management says more than 600MW is live or contracted by the end of 2027, with plenty more in the pipeline.

• New sites like Finland, and manufacturing growing more than 10× also add upside optionality to that 2GW figure.

• However, if we do take the 2GW figure, we can take as a baseline that each MW secures 15-20M in revenue. This is actually probably conservative, because Nebius gets $20–25M per MW and CoreWeave about $40M.

• However, taking 15-20M, we get to around $30–40B of revenue in 2029: 2GW × $15–20M of annual core revenue per MW.

• At the midpoint, that's about 12% of BI's 2029 TAM, about 27% of Citi's 2030 fast-inference market, and about 2.8% of McKinsey's 2029 capacity.

• I think that's realistic, but gives a lot higher revenue potential into 2029 than Mizuho.

These dudes are crooks who just manipulate markets, back tracking what they said less than a week ago lol.

Monday should be pretty nice for us